Research within the agricultural community has shown that management of crop production may be optimized by taking into account spatial variations that often exist within a given farming field. For example, by varying the farming inputs applied to a field according to local conditions within the field, a farmer can optimize crop yield as a function of the inputs being applied while preventing or minimizing environmental damage. This management technique has become known as precision, site-specific, prescription or spatially-variable farming.
The management of a field using precision farming techniques requires the gathering and processing of data relating to site-specific characteristics of the field. Generally, site-specific input data is analyzed in real-time or off-line to generate a prescription map including desired application or control rates of a farming input. A control system reads data from the prescription map and generates a control signal which is applied to a variable-rate controller adapted to apply a farming input to the field at a rate that varies as a function of the location. Variable-rate controllers may be mounted on agricultural vehicles with attached variable-rate applicators, and may be used to control application rates for applying seed, fertilizer, insecticide, herbicide or other inputs. The effect of the inputs may be analyzed by gathering site-specific yield and moisture content data and correlating this data with the farming inputs, thereby allowing a user to optimize the amounts and combinations of farming inputs applied to the field.
The spatially-variable characteristic data may be obtained by manual measuring, remote sensing, or sensing during field operations. Manual measurements typically involve taking a soil probe and analyzing the soil in a laboratory to determine nutrient data or soil condition data such as soil type or soil classification. Taking manual measurements, however, is labor intensive and, due to high sampling costs, provides only a limited number of data samples. Remote sensing may include taking aerial photographs or generating spectral images or maps from airborne or spaceborne multispectral sensors. Spectral data from remote sensing, however, is often difficult to correlate with a precise position in a field or with a specific quantifiable characteristic of the field. Both manual measurements and remote sensing require a user to conduct an airborne or ground-based survey of the field apart from normal field operations.
Spatially-variable characteristic data may also be acquired during normal field operations using appropriate sensors supported by a combine, tractor or other vehicle. A variety of characteristics may be sensed including soil properties (e.g., organic matter, fertility, nutrients, moisture content, compaction, topography or altitude), crop properties (e.g., height, moisture content or yield), and farming inputs applied to the field (e.g., fertilizers, herbicides, insecticides, seeds, cultural practices or tillage parameters and techniques used). Other spatially-variable characteristics may be manually sensed as a field is traversed (e.g., insect or weed infestation or landmarks). As these examples show, characteristics which correlate to a specific location include data related to local conditions of the field, farming inputs applied to the field, and crops harvested from the field.
The acquisition of site-specific farming data during normal field operations, however, may be subject to intermittent conditions which cause spurious farming data. For example, positioning signals transmitted to an agricultural vehicle from satellites or a ground station may be temporarily blocked or shadowed by trees, forest boundaries, buildings, or topographical features (e.g., mountains). Electrical noise (e.g., caused by a change in load on a vehicle's power system) may interfere with characteristic or location signals. Intermittent faults in a vehicle's wiring or connectors may also cause spurious characteristic or location data. Spurious farming data caused by these and other conditions are likely given the harsh environment in which agricultural vehicles typically operate.
Spurious farming data may interfere with various functions of a site-specific farming system. For example, spurious farming data may adversely affect the performance of a statistical analysis of the farming data (e.g., the determination of the average yield or moisture content of an area of a field), the generation of an electronic map showing visible indicia of a characteristic (e.g., the generation of a yield map), the generation of a prescription map based upon the farming data, or the generation of variable-rate application signals based upon the farming data.
In view of the foregoing, it would be desirable to provide a site-specific farming system which adequately accommodates spurious farming data. Furthermore, it would be desirable to provide a site-specific farming system which handles an intermittent loss of location data without switching to a dead-reckoning system to determine the current location of a vehicle based upon a last valid location, a sensed speed and a sensed direction. Such dead reckoning systems require extra sensors to sense parameters such as direction, which increase the cost and complexity thereof. In addition, such a dead reckoning based system does not accommodate spurious characteristic data, and does not efficiently use information and relationships which exist in valid site-specific farming data that has been collected.